Change Detection with LiDAR
Data
David Streutker
Idaho State University
Boise Center Aerospace Lab
Naval Postgraduate School
LiDAR Littoral Studies Workshop
Monterey, California
May 24, 2007
Introduction
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Uses of change detection
LiDAR accuracy and change detection
Two methods of co-registration
Co-registration example
Change detection of a landslide
Change detection of a rangeland fire
Uses of Change Detection
• Coastal studies
– Beach erosion and/or deposition
• Hydrology
– Water levels (surface and groundwater)
– Snow pack
– Bathymetry
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Aeolian transport
Tectonic and landslide movement
Volcanology
Vegetation monitoring
LiDAR Accuracy
• Accuracy determines amount of change
detection possible
• Absolute accuracy
– Accuracy with respect to global coordinate system
– Generally around 15 cm vertical and 50 cm
horizontal
• Relative accuracy
– Accuracy within dataset (“point-to-point”)
– Can be better than 5 cm vertical
Co-Registration
• Necessary for change detection
• “Brute force” method
– Use of least-squares to evaluate fit
– Iterative to determine best fit
– Computationally expensive
• Slope-based method
– Intelligent
• Estimates overall offset
– Flexible
• Able to use polynomial warping
– Computationally efficient
Salmon Falls Creek Landslide
Salmon Falls Creek Landslide
• Data acquired in 2002 and 2005
• 2002 data
– 1 m spacing
– High relative accuracy (< 25 cm vertical)
– NAD 27 datum
• 2005 data
– 0.5 m spacing
– Very high relative accuracy (< 10 cm vertical)
– NAD 83 datum
Accuracy of 2002 Data
• Relative accuracy “poorer” than 2005 data
• Primary reason due to small errors in flightline
co-registration
– Difficulty due to rugged terrain
• Problem: Relative accuracy on the order of or
lower than the expected change
• Solution: Redo flightline co-registration
Flightline Overlap Analysis
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Vertical Difference
Example: No Offset
Example: X Offset = 0.5 m
Example: X Offset = 1 m
Example: X Offset = 2.5 m
Example: X Offset = 5 m
X Offset = 1 m, Z Offset = 1 m
Vertical Offset Versus Slope
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Linear relationship implies shift
Shift amount in X and Y can be determined by the slope
Offset in Z determined from flat regions
Surfaces can be corrected by shifting in X, Y, and Z
Data Density
Before and After Correction
Before
After
Average Offsets
Before
After
• Vertical offset
measured from
flat areas
• Horizontal offset
measured from
steep areas
• Distribution of
offsets provides
measure of
relative accuracy
Recent Landslide Activity
Components of the slide
Landslide Change Detection
• Co-registration technique applied to 2002 and
2005 datasets
– Areas of known change were masked to avoid bias
• Landslide
• Ponds and lakes
• Quarry
– Used a robust, least absolute deviation to avoid bias
from outliers
• Co-registered images were subtracted from one
another to determine change
Comparison of Profiles
Overall Vertical Shift
-0.5
0
Vertical Difference (m)
+0.5
Deconvolving Horizontal Movement
X Offset = -78 cm
Z Offset = 12 cm
U.S. Sheep Experiment Station
• Near Dubois, Idaho
• Vegetation heights of
50 - 150 cm
• Major Species
– Mountain Sagebrush,
Rabbitbrush, Horsebrush
– Thickspike wheatgrass,
Plains reedgrass, Idaho
fescue
USDA Sheep Experiment Station
• A prescribed burn took place in the fall of
2005
• LiDAR data were acquired in the weeks
before the burn, and again soon after the
burn
• Vegetation heights were determined from
both the pre- and post-burn data
• Surface texture products were compared to
estimate burn severity
Vegetation Roughness: 1D
Vegetation Roughness: 2D
Before
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After
Vegetation Roughness (cm)
20
Vegetation Change
• Clear burn signature
• Variations in the
amount of change
indicate burn severity
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Decrease in
Roughness (cm)
15
Field Validation
• Burn severity
was measured
in the field
• Measurements
compare well to
change in
roughness
Sources of Error
• Accuracy of individual datasets
• Resolution of individual datasets
• Accuracy of co-registration
– Co-registration method
– Degree of warping used
– Unknown areas of change which bias the coregistration
– Number of points used
– Number of iterations
Conclusions
• LiDAR can be used effectively to detect and
monitor change at the sub-meter level
• LiDAR-based change detection can be used in
a variety of environments
• Statistical methods are useful for leveraging the
large amounts of data in LiDAR studies
• Care must be taken to preserve the highaccuracy of the raw LiDAR data
Questions?